The Survival Divide: Does Economic Growth Guarantee Health?

Photo of a Child vaccination program

Click on the photo to open UNICEF official page

Table of Contents

• Introduction
• Research Question and Objective
• Analysis and Interpretation
• Key Insights
• Recommendation and Future Research
• Conclusion

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Introduction

The worldwide progress in economy has not produced equivalent improvements in health status between different groups. Multiple nations now experience greater economic wealth yet people question if these resources lead to death rate reductions and expanded medical services.

This research examines the survival divide through measles vaccination coverage (MCV1), which serves as a vital indicator of child health together with public health system strength.

Understanding Measles and Vaccination

Measles is a highly contagious viral disease that primarily affects young children and can lead to severe health complications such as pneumonia, encephalitis, and even death.
Despite being preventable through vaccination, measles remains a major public health concern in many regions, especially where healthcare access is limited.

The measles vaccine (MCV1) is widely recognized as one of the most cost-effective health interventions, providing immunity and reducing mortality rates significantly.
Monitoring vaccination coverage not only reflects a nation’s healthcare system efficiency but also highlights broader issues of equity, access, and the ability to translate economic progress into tangible health outcomes.

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Research Question and Objective

Core Question

Does Economic Growth Guarantee Better Health Outcomes?

Objectives

• Analyze global patterns between GDP per capita and life expectancy.

• To highlight disparities where economic wealth does not translate into improved health outcomes.

• To explore the role of healthcare infrastructure for examaple hospital beds in supporting survival gains.

• To identify actionable recommendations for bridging the survival divide

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Analysis and Interpretation

Economic development provides resources, but it does not guarantee that these resources are effectively used to improve public health. Countries with robust healthcare systems — including widespread access to hospitals and clinics — are far better positioned to ensure high measles immunization rates, regardless of their income classification.

Global Map of Measles Vaccination Coverage (MCV1) Over Time

The visual presentation below analyzes UNICEF statistics explores the percentage of surviving infants who recieved the first dose of the Measles- containing vaccine (MCV1), a crucial step in preventing deadly outbreaks. The eavaluation of survival patterns across periods and country reveals how global child health shape both strong outcomes and unequal distribution of results.

import pandas as pd
import plotly.express as px

df = pd.read_csv('unicef_indicator_1.csv')
df = df[df['indicator'].str.contains("measles", case=False, na=False)]

fig = px.choropleth(
    df,
    locations="alpha_3_code",
    color="obs_value",
    hover_name="country",
    animation_frame="time_period",  
    color_continuous_scale="Reds",
    title="MCV1 Vaccination Coverage Over Time",
    labels={"obs_value": "Vaccination (%)"}
)

fig.update_layout(
    height=600,
    
    font=dict(family="Arial", size=7),
   
)

from IPython.display import HTML
HTML(fig.to_html(include_plotlyjs='cdn'))

This animated choropleth map visualizes the global progression of measles vaccination coverage (MCV1) over different years. Each frame represents a snapshot of MCV1 vaccination rates across countries for a specific year, providing a dynamic overview of how immunization efforts have evolved globally.

Higher vaccination percentages (depicted in darker shades of red) indicate stronger protection against measles outbreaks, a critical marker of public health system performance. This visualization highlights regional trends, identifies areas with persistent low coverage, and underscores the progress made toward universal immunization goals.

Connection between Infant Survival Rate and GDP

The connection shows why strong national economies improve healthcare services. Economic success helps people get better healthcare treatments including vaccines that lead to better child health results. Strong Public health systems combined with good economic conditions explain the top-performing nations because they provide good access to healthcare and implement effective vaccines.

import pandas as pd
import plotly.express as px

df_survival = pd.read_csv('unicef_indicator_1.csv')
df_gdp = pd.read_csv('unicef_metadata.csv')

df_survival = df_survival[df_survival['indicator'].str.contains("measles", case=False, na=False)]

merged = pd.merge(
    df_survival,
    df_gdp[['country', 'GDP per capita (constant 2015 US$)']],
    on='country',
    how='inner'
).rename(columns={'GDP per capita (constant 2015 US$)': 'gdp_per_capita'})

merged = merged.dropna(subset=['obs_value', 'gdp_per_capita'])

fig = px.scatter(
    merged,
    x='obs_value',
    y='gdp_per_capita',
    color='country',
    hover_name='country',
    title='Infant Survival Rate vs. GDP per Capita',
    labels={'obs_value': 'Infant Survival Rate (%)', 'gdp_per_capita': 'GDP per Capita (USD)'},
    template='plotly_white'
)


fig.update_layout(
    height=400,
   
    font=dict(family="Arial", size=5),
   
)

from IPython.display import HTML
HTML(fig.to_html(include_plotlyjs='cdn'))

The graph demonstrates that Monaco maintains higher than average economic growth rates alongside competitive healthcare performance. Though countries like Kazakhstan, Belrus and Slovakia generate smaller amounts of economic value have achieved top performance in infant survival numbers.

“ECONOMIC STRENGTH DOES NOT DECIDE A NATION’S HEALTHCARE ACHIEVEMENT”

Vaccination programs along with government policies and healthcare systems determine better chances of child survival more effectively than income levels. Global health organisations UNICEF and WHO report that vaccination programssave the greatest number of lives for the lowest cost in child health care. (UNICEF, 2023; WHO, 2022)

National health strategies that invest money wisely bring better survival results for children without the resources of wealthy nations.

GDP and Survival Rate of Somalia v/s Chad

For example let’s analyse two countries Somalia and Chad which have similar level of Gross Domestic Product (GDP) to see if economic growth promotes higher infant death rates.

import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots

df_survival = pd.read_csv('unicef_indicator_1.csv')
df_gdp = pd.read_csv('unicef_metadata.csv')

# Filtered survival data for Somalia and Chad
df_survival = df_survival[
    (df_survival['indicator'].str.contains("measles", case=False, na=False)) &
    (df_survival['country'].isin(['Somalia', 'Chad']))
]

# Filtered GDP data for Somalia and Chad
df_gdp = df_gdp[df_gdp['country'].isin(['Somalia', 'Chad'])]

df_gdp = df_gdp[['country', 'year', 'GDP per capita (constant 2015 US$)']]
df_survival = df_survival[['country', 'time_period', 'obs_value']]

df_gdp = df_gdp.rename(columns={'GDP per capita (constant 2015 US$)': 'gdp_per_capita'})
df_survival = df_survival.rename(columns={'time_period': 'year', 'obs_value': 'survival_rate'})

merged = pd.merge(df_survival, df_gdp, on=['country', 'year'], how='inner')

# Created subplots
fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
                    vertical_spacing=0.1,
                    subplot_titles=("Sum of Infant Survival Rate", "GDP per Capita (Constant USD)"))

# Plot survival rate
for country in ['Chad', 'Somalia']:
    df_country = merged[merged['country'] == country]
    fig.add_trace(
        go.Scatter(x=df_country['year'], y=df_country['survival_rate'],
                   mode='lines+markers', name=country),
        row=1, col=1
    )

# Plot GDP per capita
for country in ['Chad', 'Somalia']:
    df_country = merged[merged['country'] == country]
    fig.add_trace(
        go.Scatter(x=df_country['year'], y=df_country['gdp_per_capita'],
                   mode='lines+markers', name=country, showlegend=False),
        row=2, col=1
    )

fig.update_layout(
    height=400,
    title_text="GDP and Survival Rate of Somalia v/s Chad",
    template='plotly_white',
    font=dict(family="Arial", size=5),
    margin=dict(t=30, b=50),
    uirevision = True
)


fig.update_yaxes(title_text="Sum of Infant Survival Rate", row=1, col=1)
fig.update_yaxes(title_text="GDP per Capita (constant USD)", row=2, col=1)

from IPython.display import HTML
HTML(fig.to_html(include_plotlyjs='cdn',full_html = False))

WHAT DID WE LEARN?

Despite being one of the least financially successful nation, Somalia has shown good healthcare results as seen in it’s survival rate which grow from 9% to 46% and stayed at that level since 2010. Chad’s growing economy did not lead to better child survival rates even after being at a stronger financial position.

Top 5 and Bottom 5 Countries by Hospital Beds Availability

To better understand the healthcare infrastructure globally, we analyzed the availability of hospital beds, measured per 1,000 people.

The chart below presents a comparison between the top 5 countries (with the highest average hospital bed availability) and the bottom 5 countries (with the lowest availability).
> > High hospital bed density often correlates with stronger healthcare systems, greater capacity to manage disease outbreaks, and improved access to critical care services. Conversely, countries with limited hospital infrastructure may struggle to deliver timely and effective healthcare, exacerbating health disparities despite economic growth.

import pandas as pd
import plotly.graph_objects as go

df_vaccine = pd.read_csv("unicef_indicator_1.csv")
df_meta = pd.read_csv("unicef_metadata.csv")

df_meta = df_meta[['country', 'Hospital beds (per 1,000 people)']]
df_beds_avg = df_meta.groupby('country', as_index=False)['Hospital beds (per 1,000 people)'].mean()
df_beds_avg = df_beds_avg.rename(columns={'Hospital beds (per 1,000 people)': 'avg_hospital_beds_per_1000'})


top5 = df_beds_avg.sort_values('avg_hospital_beds_per_1000', ascending=False).head(5)
bottom5 = df_beds_avg.sort_values('avg_hospital_beds_per_1000').head(5)

# Inverted bottom values for downward bars
bottom5['avg_hospital_beds_per_1000'] *= -1

top5['label'] = 'High'
bottom5['label'] = 'Low'
combined = pd.concat([top5, bottom5], ignore_index=True)

combined['country'] = pd.Categorical(combined['country'], categories=combined.sort_values('avg_hospital_beds_per_1000')['country'], ordered=True)

fig = go.Figure()

fig.add_trace(go.Bar(
    x=combined['country'],
    y=combined['avg_hospital_beds_per_1000'],
    marker_color=combined['label'].map({'High': 'green', 'Low': 'crimson'}),
    text=combined['avg_hospital_beds_per_1000'].abs().round(2),
    textposition='outside',
    name='Hospital Beds',
))

fig.update_layout(
    title="Top 5 vs Bottom 5 Countries by Hospital Beds",
    yaxis_title="Beds per 1,000 (Negative = Lowest)",
    xaxis_title="Country",
    template='plotly_white',
    height=500,
    width=400,
    title_x=0.5,
    showlegend=False,
    font=dict(size=6),
    margin=dict(l=20, r=20, t=20, b=20),
)

fig.update_yaxes(zeroline=True, zerolinewidth=2, zerolinecolor='gray')

from IPython.display import HTML
HTML(fig.to_html(include_plotlyjs='cdn',full_html = False))

Key Insights

• Global Correlation

• Economic Growth ≠ Guaranteed Health

• Healthcare Infrastructure Matters

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Recommendation and Future Research

• Strengthen Healthcare Infrastructure: Expand hospitals, rural clinics, and healthcare workforce to bridge gaps in access and service delivery.

• Promote Inclusive Health Policies: Ensure economic growth directly benefits marginalized populations through equitable vaccination and care initiatives.

• Address Socioeconomic Inequalities: Tackle income, education, and regional disparities that weaken health outcomes despite national wealth.

• Enhance Health Data and Monitoring: Build robust data systems to track vaccination coverage, healthcare access, and identify priority intervention areas.

• Future Research Direction: Conduct longitudinal studies and country case analyses to understand how economic, political, and cultural factors shape long-term health outcomes.

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Conclusion

Economic growth alone is not a universal guarantee of better health.

Structural, policy, and cultural factors are pivotal for translating wealth into well-being.

Data-driven approaches are essential for achieving sustainable development and health equity.

The case of measles vaccination demonstrates that intentional healthcare investments, equity-driven policies, and public trust-building efforts are pivotal for converting national wealth into genuine survival gains. Bridging the survival divide requires more than prosperity — it demands political will, public engagement, and systems built to serve every citizen.

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© 2025 Bhagyashree Nidiyoor